Overview

Brought to you by YData

Dataset statistics

Number of variables74
Number of observations799869
Missing cells799285
Missing cells (%)1.4%
Total size in memory451.6 MiB
Average record size in memory592.0 B

Variable types

Text34
Numeric40

Alerts

ANIO_REGIS has constant value "2023" Constant
RAZON_M has constant value "1.0" Constant
RAZON_M has 799285 (99.9%) missing values Missing
ANIO_OCUR is highly skewed (γ1 = 52.7618851) Skewed
LUGAR_OCUR has 14218 (1.8%) zeros Zeros
HORAS has 31107 (3.9%) zeros Zeros
MINUTOS has 198635 (24.8%) zeros Zeros

Reproduction

Analysis started2025-08-22 23:40:07.259964
Analysis finished2025-08-22 23:40:18.022487
Duration10.76 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:18.105892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1599738
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row01
2nd row01
3rd row01
4th row01
5th row01
ValueCountFrequency (%)
15 87642
 
11.0%
09 78625
 
9.8%
30 59674
 
7.5%
14 53852
 
6.7%
21 41736
 
5.2%
11 40079
 
5.0%
19 37278
 
4.7%
07 30576
 
3.8%
16 30066
 
3.8%
08 27685
 
3.5%
Other values (22) 312656
39.1%
2025-08-22T17:40:18.227232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 422519
26.4%
0 302025
18.9%
2 258745
16.2%
5 124510
 
7.8%
9 123245
 
7.7%
3 116484
 
7.3%
4 77901
 
4.9%
7 61269
 
3.8%
8 56726
 
3.5%
6 56314
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1599738
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 422519
26.4%
0 302025
18.9%
2 258745
16.2%
5 124510
 
7.8%
9 123245
 
7.7%
3 116484
 
7.3%
4 77901
 
4.9%
7 61269
 
3.8%
8 56726
 
3.5%
6 56314
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1599738
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 422519
26.4%
0 302025
18.9%
2 258745
16.2%
5 124510
 
7.8%
9 123245
 
7.7%
3 116484
 
7.3%
4 77901
 
4.9%
7 61269
 
3.8%
8 56726
 
3.5%
6 56314
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1599738
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 422519
26.4%
0 302025
18.9%
2 258745
16.2%
5 124510
 
7.8%
9 123245
 
7.7%
3 116484
 
7.3%
4 77901
 
4.9%
7 61269
 
3.8%
8 56726
 
3.5%
6 56314
 
3.5%
Distinct539
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:18.397108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2399607
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)< 0.1%

Sample

1st row001
2nd row001
3rd row001
4th row001
5th row001
ValueCountFrequency (%)
039 59993
 
7.5%
015 46811
 
5.9%
010 26224
 
3.3%
004 25272
 
3.2%
005 23067
 
2.9%
001 20678
 
2.6%
002 19740
 
2.5%
030 17494
 
2.2%
106 17160
 
2.1%
007 17087
 
2.1%
Other values (529) 526343
65.8%
2025-08-22T17:40:18.615467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 944940
39.4%
1 374563
 
15.6%
3 216880
 
9.0%
5 165775
 
6.9%
9 143013
 
6.0%
2 138619
 
5.8%
4 134525
 
5.6%
7 101278
 
4.2%
8 100569
 
4.2%
6 79445
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2399607
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 944940
39.4%
1 374563
 
15.6%
3 216880
 
9.0%
5 165775
 
6.9%
9 143013
 
6.0%
2 138619
 
5.8%
4 134525
 
5.6%
7 101278
 
4.2%
8 100569
 
4.2%
6 79445
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2399607
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 944940
39.4%
1 374563
 
15.6%
3 216880
 
9.0%
5 165775
 
6.9%
9 143013
 
6.0%
2 138619
 
5.8%
4 134525
 
5.6%
7 101278
 
4.2%
8 100569
 
4.2%
6 79445
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2399607
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 944940
39.4%
1 374563
 
15.6%
3 216880
 
9.0%
5 165775
 
6.9%
9 143013
 
6.0%
2 138619
 
5.8%
4 134525
 
5.6%
7 101278
 
4.2%
8 100569
 
4.2%
6 79445
 
3.3%

TLOC_REGIS
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.54516427
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:18.670593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median14
Q315
95-th percentile17
Maximum17
Range16
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.618101452
Coefficient of variation (CV)0.400003096
Kurtosis-0.7592059406
Mean11.54516427
Median Absolute Deviation (MAD)2
Skewness-0.7769983663
Sum9234619
Variance21.32686102
MonotonicityNot monotonic
2025-08-22T17:40:18.713290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
15 186016
23.3%
14 89887
11.2%
13 83988
10.5%
16 68701
 
8.6%
17 55884
 
7.0%
5 46169
 
5.8%
4 40026
 
5.0%
6 33219
 
4.2%
8 30035
 
3.8%
9 29189
 
3.6%
Other values (7) 136755
17.1%
ValueCountFrequency (%)
1 13804
 
1.7%
2 27804
3.5%
3 8309
 
1.0%
4 40026
5.0%
5 46169
5.8%
ValueCountFrequency (%)
17 55884
 
7.0%
16 68701
 
8.6%
15 186016
23.3%
14 89887
11.2%
13 83988
10.5%
Distinct311
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:18.887701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3199476
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0001
2nd row0001
3rd row0001
4th row0001
5th row0001
ValueCountFrequency (%)
0001 735399
91.9%
0039 7250
 
0.9%
0003 2686
 
0.3%
0009 2300
 
0.3%
0016 1822
 
0.2%
0014 1593
 
0.2%
0002 1513
 
0.2%
0013 1273
 
0.2%
0034 1185
 
0.1%
0029 1078
 
0.1%
Other values (301) 43770
 
5.5%
2025-08-22T17:40:19.114333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2336260
73.0%
1 758141
 
23.7%
3 22567
 
0.7%
9 16174
 
0.5%
2 15796
 
0.5%
4 13101
 
0.4%
7 10788
 
0.3%
5 9793
 
0.3%
6 8960
 
0.3%
8 7896
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3199476
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2336260
73.0%
1 758141
 
23.7%
3 22567
 
0.7%
9 16174
 
0.5%
2 15796
 
0.5%
4 13101
 
0.4%
7 10788
 
0.3%
5 9793
 
0.3%
6 8960
 
0.3%
8 7896
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3199476
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2336260
73.0%
1 758141
 
23.7%
3 22567
 
0.7%
9 16174
 
0.5%
2 15796
 
0.5%
4 13101
 
0.4%
7 10788
 
0.3%
5 9793
 
0.3%
6 8960
 
0.3%
8 7896
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3199476
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2336260
73.0%
1 758141
 
23.7%
3 22567
 
0.7%
9 16174
 
0.5%
2 15796
 
0.5%
4 13101
 
0.4%
7 10788
 
0.3%
5 9793
 
0.3%
6 8960
 
0.3%
8 7896
 
0.2%
Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:19.197459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1599738
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row32
2nd row01
3rd row01
4th row01
5th row14
ValueCountFrequency (%)
15 96278
 
12.0%
09 65291
 
8.2%
30 60431
 
7.6%
14 52709
 
6.6%
21 41676
 
5.2%
11 40090
 
5.0%
19 35813
 
4.5%
07 30742
 
3.8%
16 30377
 
3.8%
20 27998
 
3.5%
Other values (24) 318464
39.8%
2025-08-22T17:40:19.304189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 428997
26.8%
0 286579
17.9%
2 254698
15.9%
5 132542
 
8.3%
9 127348
 
8.0%
3 120482
 
7.5%
4 76900
 
4.8%
7 60887
 
3.8%
6 55746
 
3.5%
8 55559
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1599738
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 428997
26.8%
0 286579
17.9%
2 254698
15.9%
5 132542
 
8.3%
9 127348
 
8.0%
3 120482
 
7.5%
4 76900
 
4.8%
7 60887
 
3.8%
6 55746
 
3.5%
8 55559
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1599738
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 428997
26.8%
0 286579
17.9%
2 254698
15.9%
5 132542
 
8.3%
9 127348
 
8.0%
3 120482
 
7.5%
4 76900
 
4.8%
7 60887
 
3.8%
6 55746
 
3.5%
8 55559
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1599738
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 428997
26.8%
0 286579
17.9%
2 254698
15.9%
5 132542
 
8.3%
9 127348
 
8.0%
3 120482
 
7.5%
4 76900
 
4.8%
7 60887
 
3.8%
6 55746
 
3.5%
8 55559
 
3.5%
Distinct568
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:19.479187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2399607
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row044
2nd row001
3rd row001
4th row001
5th row053
ValueCountFrequency (%)
039 28482
 
3.6%
007 25262
 
3.2%
005 25122
 
3.1%
004 23252
 
2.9%
002 21692
 
2.7%
001 20894
 
2.6%
006 18863
 
2.4%
017 17686
 
2.2%
003 16383
 
2.0%
033 15889
 
2.0%
Other values (558) 586344
73.3%
2025-08-22T17:40:19.701668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 944206
39.3%
1 344725
 
14.4%
3 201296
 
8.4%
2 177491
 
7.4%
9 144023
 
6.0%
5 136648
 
5.7%
4 134563
 
5.6%
7 123259
 
5.1%
8 102407
 
4.3%
6 90989
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2399607
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 944206
39.3%
1 344725
 
14.4%
3 201296
 
8.4%
2 177491
 
7.4%
9 144023
 
6.0%
5 136648
 
5.7%
4 134563
 
5.6%
7 123259
 
5.1%
8 102407
 
4.3%
6 90989
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2399607
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 944206
39.3%
1 344725
 
14.4%
3 201296
 
8.4%
2 177491
 
7.4%
9 144023
 
6.0%
5 136648
 
5.7%
4 134563
 
5.6%
7 123259
 
5.1%
8 102407
 
4.3%
6 90989
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2399607
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 944206
39.3%
1 344725
 
14.4%
3 201296
 
8.4%
2 177491
 
7.4%
9 144023
 
6.0%
5 136648
 
5.7%
4 134563
 
5.6%
7 123259
 
5.1%
8 102407
 
4.3%
6 90989
 
3.8%

TLOC_RESID
Real number (ℝ)

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.33152804
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:19.756197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median13
Q315
95-th percentile17
Maximum99
Range98
Interquartile range (IQR)10

Descriptive statistics

Standard deviation12.2329704
Coefficient of variation (CV)1.079551704
Kurtosis37.30206634
Mean11.33152804
Median Absolute Deviation (MAD)4
Skewness5.501872122
Sum9063738
Variance149.6455649
MonotonicityNot monotonic
2025-08-22T17:40:19.797731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
15 128617
16.1%
1 95856
12.0%
14 93257
11.7%
13 64002
8.0%
17 62511
7.8%
16 46655
 
5.8%
5 44014
 
5.5%
2 42634
 
5.3%
4 39844
 
5.0%
6 27475
 
3.4%
Other values (8) 155004
19.4%
ValueCountFrequency (%)
1 95856
12.0%
2 42634
5.3%
3 13296
 
1.7%
4 39844
5.0%
5 44014
5.5%
ValueCountFrequency (%)
99 12190
 
1.5%
17 62511
7.8%
16 46655
 
5.8%
15 128617
16.1%
14 93257
11.7%
Distinct869
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:19.961663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3199476
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0001
2nd row0001
3rd row0001
4th row0001
5th row7777
ValueCountFrequency (%)
0001 591412
73.9%
7777 33125
 
4.1%
9999 12187
 
1.5%
0002 6042
 
0.8%
0003 5544
 
0.7%
0004 4460
 
0.6%
0005 4422
 
0.6%
0006 3901
 
0.5%
0008 3842
 
0.5%
0011 3530
 
0.4%
Other values (859) 131404
 
16.4%
2025-08-22T17:40:20.184530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2122337
66.3%
1 657881
 
20.6%
7 155030
 
4.8%
9 68363
 
2.1%
2 47198
 
1.5%
3 39138
 
1.2%
4 33368
 
1.0%
5 28008
 
0.9%
6 25370
 
0.8%
8 22783
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3199476
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2122337
66.3%
1 657881
 
20.6%
7 155030
 
4.8%
9 68363
 
2.1%
2 47198
 
1.5%
3 39138
 
1.2%
4 33368
 
1.0%
5 28008
 
0.9%
6 25370
 
0.8%
8 22783
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3199476
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2122337
66.3%
1 657881
 
20.6%
7 155030
 
4.8%
9 68363
 
2.1%
2 47198
 
1.5%
3 39138
 
1.2%
4 33368
 
1.0%
5 28008
 
0.9%
6 25370
 
0.8%
8 22783
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3199476
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2122337
66.3%
1 657881
 
20.6%
7 155030
 
4.8%
9 68363
 
2.1%
2 47198
 
1.5%
3 39138
 
1.2%
4 33368
 
1.0%
5 28008
 
0.9%
6 25370
 
0.8%
8 22783
 
0.7%
Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:20.269935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1599738
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row01
2nd row01
3rd row01
4th row01
5th row01
ValueCountFrequency (%)
15 87511
 
10.9%
09 78484
 
9.8%
30 59530
 
7.4%
14 53706
 
6.7%
21 41644
 
5.2%
11 40048
 
5.0%
19 37101
 
4.6%
07 30552
 
3.8%
16 29903
 
3.7%
20 27381
 
3.4%
Other values (23) 314009
39.3%
2025-08-22T17:40:20.380098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 421245
26.3%
0 300337
18.8%
2 257498
16.1%
9 129596
 
8.1%
5 124260
 
7.8%
3 116121
 
7.3%
4 77604
 
4.9%
7 61243
 
3.8%
6 56064
 
3.5%
8 55770
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1599738
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 421245
26.3%
0 300337
18.8%
2 257498
16.1%
9 129596
 
8.1%
5 124260
 
7.8%
3 116121
 
7.3%
4 77604
 
4.9%
7 61243
 
3.8%
6 56064
 
3.5%
8 55770
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1599738
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 421245
26.3%
0 300337
18.8%
2 257498
16.1%
9 129596
 
8.1%
5 124260
 
7.8%
3 116121
 
7.3%
4 77604
 
4.9%
7 61243
 
3.8%
6 56064
 
3.5%
8 55770
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1599738
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 421245
26.3%
0 300337
18.8%
2 257498
16.1%
9 129596
 
8.1%
5 124260
 
7.8%
3 116121
 
7.3%
4 77604
 
4.9%
7 61243
 
3.8%
6 56064
 
3.5%
8 55770
 
3.5%
Distinct566
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:20.559526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2399607
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row001
2nd row001
3rd row001
4th row001
5th row001
ValueCountFrequency (%)
039 43101
 
5.4%
005 27362
 
3.4%
007 26032
 
3.3%
004 25879
 
3.2%
002 24319
 
3.0%
001 21261
 
2.7%
006 19042
 
2.4%
014 17230
 
2.2%
017 16963
 
2.1%
020 16392
 
2.0%
Other values (556) 562288
70.3%
2025-08-22T17:40:20.787483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 958587
39.9%
1 344994
 
14.4%
3 205658
 
8.6%
2 169570
 
7.1%
4 140625
 
5.9%
5 139119
 
5.8%
9 133456
 
5.6%
7 119230
 
5.0%
8 96916
 
4.0%
6 91452
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2399607
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 958587
39.9%
1 344994
 
14.4%
3 205658
 
8.6%
2 169570
 
7.1%
4 140625
 
5.9%
5 139119
 
5.8%
9 133456
 
5.6%
7 119230
 
5.0%
8 96916
 
4.0%
6 91452
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2399607
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 958587
39.9%
1 344994
 
14.4%
3 205658
 
8.6%
2 169570
 
7.1%
4 140625
 
5.9%
5 139119
 
5.8%
9 133456
 
5.6%
7 119230
 
5.0%
8 96916
 
4.0%
6 91452
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2399607
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 958587
39.9%
1 344994
 
14.4%
3 205658
 
8.6%
2 169570
 
7.1%
4 140625
 
5.9%
5 139119
 
5.8%
9 133456
 
5.6%
7 119230
 
5.0%
8 96916
 
4.0%
6 91452
 
3.8%

TLOC_OCURR
Real number (ℝ)

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.47742818
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:20.842893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q17
median13
Q315
95-th percentile17
Maximum99
Range98
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.476867559
Coefficient of variation (CV)0.7385685562
Kurtosis62.91187966
Mean11.47742818
Median Absolute Deviation (MAD)3
Skewness6.104045432
Sum9180439
Variance71.85728361
MonotonicityNot monotonic
2025-08-22T17:40:20.884539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
15 146071
18.3%
14 115528
14.4%
13 77426
9.7%
1 71541
8.9%
17 66875
8.4%
16 59652
7.5%
5 32733
 
4.1%
2 31394
 
3.9%
4 30333
 
3.8%
9 26366
 
3.3%
Other values (8) 141950
17.7%
ValueCountFrequency (%)
1 71541
8.9%
2 31394
3.9%
3 12066
 
1.5%
4 30333
3.8%
5 32733
4.1%
ValueCountFrequency (%)
99 4614
 
0.6%
17 66875
8.4%
16 59652
7.5%
15 146071
18.3%
14 115528
14.4%
Distinct712
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:21.049976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3199476
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0001
2nd row0001
3rd row0001
4th row0001
5th row0001
ValueCountFrequency (%)
0001 644880
80.6%
7777 30553
 
3.8%
0002 4732
 
0.6%
9999 4611
 
0.6%
0003 4193
 
0.5%
0005 3362
 
0.4%
0004 3169
 
0.4%
0014 2701
 
0.3%
0008 2698
 
0.3%
0006 2653
 
0.3%
Other values (702) 96317
 
12.0%
2025-08-22T17:40:21.271253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2189865
68.4%
1 693807
 
21.7%
7 137890
 
4.3%
2 35418
 
1.1%
9 34408
 
1.1%
3 30004
 
0.9%
4 25076
 
0.8%
5 19299
 
0.6%
6 17710
 
0.6%
8 15999
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3199476
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2189865
68.4%
1 693807
 
21.7%
7 137890
 
4.3%
2 35418
 
1.1%
9 34408
 
1.1%
3 30004
 
0.9%
4 25076
 
0.8%
5 19299
 
0.6%
6 17710
 
0.6%
8 15999
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3199476
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2189865
68.4%
1 693807
 
21.7%
7 137890
 
4.3%
2 35418
 
1.1%
9 34408
 
1.1%
3 30004
 
0.9%
4 25076
 
0.8%
5 19299
 
0.6%
6 17710
 
0.6%
8 15999
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3199476
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2189865
68.4%
1 693807
 
21.7%
7 137890
 
4.3%
2 35418
 
1.1%
9 34408
 
1.1%
3 30004
 
0.9%
4 25076
 
0.8%
5 19299
 
0.6%
6 17710
 
0.6%
8 15999
 
0.5%
Distinct4119
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:21.448859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3199476
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique898 ?
Unique (%)0.1%

Sample

1st rowJ189
2nd rowJ80X
3rd rowJ440
4th rowE441
5th rowK703
ValueCountFrequency (%)
i219 130456
 
16.3%
e116 34229
 
4.3%
j189 27039
 
3.4%
e117 18392
 
2.3%
e112 16464
 
2.1%
k746 16045
 
2.0%
j449 12036
 
1.5%
x954 11708
 
1.5%
k703 9630
 
1.2%
e146 9456
 
1.2%
Other values (4109) 514414
64.3%
2025-08-22T17:40:21.661222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 519503
16.2%
9 484388
15.1%
2 287186
9.0%
I 227912
 
7.1%
0 209509
 
6.5%
4 196497
 
6.1%
6 168160
 
5.3%
8 137943
 
4.3%
E 127068
 
4.0%
7 125933
 
3.9%
Other values (23) 715377
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3199476
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 519503
16.2%
9 484388
15.1%
2 287186
9.0%
I 227912
 
7.1%
0 209509
 
6.5%
4 196497
 
6.1%
6 168160
 
5.3%
8 137943
 
4.3%
E 127068
 
4.0%
7 125933
 
3.9%
Other values (23) 715377
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3199476
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 519503
16.2%
9 484388
15.1%
2 287186
9.0%
I 227912
 
7.1%
0 209509
 
6.5%
4 196497
 
6.1%
6 168160
 
5.3%
8 137943
 
4.3%
E 127068
 
4.0%
7 125933
 
3.9%
Other values (23) 715377
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3199476
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 519503
16.2%
9 484388
15.1%
2 287186
9.0%
I 227912
 
7.1%
0 209509
 
6.5%
4 196497
 
6.1%
6 168160
 
5.3%
8 137943
 
4.3%
E 127068
 
4.0%
7 125933
 
3.9%
Other values (23) 715377
22.4%
Distinct561
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:21.839449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length0
Mean length0.4263598164
Min length0

Characters and Unicode

Total characters341032
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique150 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
s069 14527
17.0%
t71x 11711
 
13.7%
s219 5085
 
6.0%
s062 4762
 
5.6%
s018 3414
 
4.0%
t07x 3279
 
3.8%
s299 3215
 
3.8%
t149 2907
 
3.4%
t141 2383
 
2.8%
t751 1722
 
2.0%
Other values (550) 32253
37.8%
2025-08-22T17:40:22.060133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 53059
15.6%
1 42488
12.5%
9 41996
12.3%
0 41931
12.3%
T 30838
9.0%
6 29499
8.6%
7 26464
7.8%
2 22730
6.7%
X 16145
 
4.7%
8 9409
 
2.8%
Other values (9) 26473
7.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 341032
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 53059
15.6%
1 42488
12.5%
9 41996
12.3%
0 41931
12.3%
T 30838
9.0%
6 29499
8.6%
7 26464
7.8%
2 22730
6.7%
X 16145
 
4.7%
8 9409
 
2.8%
Other values (9) 26473
7.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 341032
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 53059
15.6%
1 42488
12.5%
9 41996
12.3%
0 41931
12.3%
T 30838
9.0%
6 29499
8.6%
7 26464
7.8%
2 22730
6.7%
X 16145
 
4.7%
8 9409
 
2.8%
Other values (9) 26473
7.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 341032
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 53059
15.6%
1 42488
12.5%
9 41996
12.3%
0 41931
12.3%
T 30838
9.0%
6 29499
8.6%
7 26464
7.8%
2 22730
6.7%
X 16145
 
4.7%
8 9409
 
2.8%
Other values (9) 26473
7.8%
Distinct321
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:22.250764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.932050123
Min length2

Characters and Unicode

Total characters2345256
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)< 0.1%

Sample

1st row33B
2nd row33Z
3rd row33G
4th row21C
5th row35L
ValueCountFrequency (%)
28a 131203
 
16.4%
20d 110059
 
13.8%
33b 33454
 
4.2%
55 32252
 
4.0%
35m 26883
 
3.4%
33g 18605
 
2.3%
49b 16765
 
2.1%
38c 15929
 
2.0%
51z 14129
 
1.8%
35l 13169
 
1.6%
Other values (311) 387421
48.4%
2025-08-22T17:40:22.486370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 367066
15.7%
3 318968
13.6%
0 222650
9.5%
8 190741
8.1%
5 174264
7.4%
A 173066
7.4%
D 159600
 
6.8%
1 118051
 
5.0%
Z 98470
 
4.2%
B 96775
 
4.1%
Other values (21) 425605
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2345256
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 367066
15.7%
3 318968
13.6%
0 222650
9.5%
8 190741
8.1%
5 174264
7.4%
A 173066
7.4%
D 159600
 
6.8%
1 118051
 
5.0%
Z 98470
 
4.2%
B 96775
 
4.1%
Other values (21) 425605
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2345256
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 367066
15.7%
3 318968
13.6%
0 222650
9.5%
8 190741
8.1%
5 174264
7.4%
A 173066
7.4%
D 159600
 
6.8%
1 118051
 
5.0%
Z 98470
 
4.2%
B 96775
 
4.1%
Other values (21) 425605
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2345256
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 367066
15.7%
3 318968
13.6%
0 222650
9.5%
8 190741
8.1%
5 174264
7.4%
A 173066
7.4%
D 159600
 
6.8%
1 118051
 
5.0%
Z 98470
 
4.2%
B 96775
 
4.1%
Other values (21) 425605
18.1%

SEXO
Real number (ℝ)

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.445919269
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:22.534469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile2
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5315086347
Coefficient of variation (CV)0.3675921927
Kurtosis23.60929342
Mean1.445919269
Median Absolute Deviation (MAD)0
Skewness1.98576316
Sum1156546
Variance0.2825014287
MonotonicityNot monotonic
2025-08-22T17:40:22.567869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
1 446734
55.9%
2 352629
44.1%
9 506
 
0.1%
ValueCountFrequency (%)
1 446734
55.9%
2 352629
44.1%
9 506
 
0.1%
ValueCountFrequency (%)
9 506
 
0.1%
2 352629
44.1%
1 446734
55.9%
Distinct173
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:22.642838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2399607
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)< 0.1%

Sample

1st row001
2nd row016
3rd row001
4th row001
5th row998
ValueCountFrequency (%)
009 80693
 
10.1%
030 66241
 
8.3%
014 50782
 
6.3%
015 49121
 
6.1%
021 47431
 
5.9%
011 45115
 
5.6%
016 38977
 
4.9%
020 35340
 
4.4%
007 32623
 
4.1%
012 25606
 
3.2%
Other values (163) 327940
41.0%
2025-08-22T17:40:22.764409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1063857
44.3%
1 407048
 
17.0%
2 266576
 
11.1%
9 169685
 
7.1%
3 127648
 
5.3%
5 88914
 
3.7%
4 81749
 
3.4%
8 75278
 
3.1%
6 60233
 
2.5%
7 58619
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2399607
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1063857
44.3%
1 407048
 
17.0%
2 266576
 
11.1%
9 169685
 
7.1%
3 127648
 
5.3%
5 88914
 
3.7%
4 81749
 
3.4%
8 75278
 
3.1%
6 60233
 
2.5%
7 58619
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2399607
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1063857
44.3%
1 407048
 
17.0%
2 266576
 
11.1%
9 169685
 
7.1%
3 127648
 
5.3%
5 88914
 
3.7%
4 81749
 
3.4%
8 75278
 
3.1%
6 60233
 
2.5%
7 58619
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2399607
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1063857
44.3%
1 407048
 
17.0%
2 266576
 
11.1%
9 169685
 
7.1%
3 127648
 
5.3%
5 88914
 
3.7%
4 81749
 
3.4%
8 75278
 
3.1%
6 60233
 
2.5%
7 58619
 
2.4%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:22.799488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters799869
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2
ValueCountFrequency (%)
2 710337
88.8%
9 45248
 
5.7%
8 36499
 
4.6%
1 7785
 
1.0%
2025-08-22T17:40:22.865334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 710337
88.8%
9 45248
 
5.7%
8 36499
 
4.6%
1 7785
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 799869
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 710337
88.8%
9 45248
 
5.7%
8 36499
 
4.6%
1 7785
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 799869
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 710337
88.8%
9 45248
 
5.7%
8 36499
 
4.6%
1 7785
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 799869
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 710337
88.8%
9 45248
 
5.7%
8 36499
 
4.6%
1 7785
 
1.0%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:22.891895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters799869
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2
ValueCountFrequency (%)
2 665890
83.2%
1 53651
 
6.7%
9 43829
 
5.5%
8 36499
 
4.6%
2025-08-22T17:40:22.956108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 665890
83.2%
1 53651
 
6.7%
9 43829
 
5.5%
8 36499
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 799869
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 665890
83.2%
1 53651
 
6.7%
9 43829
 
5.5%
8 36499
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 799869
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 665890
83.2%
1 53651
 
6.7%
9 43829
 
5.5%
8 36499
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 799869
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 665890
83.2%
1 53651
 
6.7%
9 43829
 
5.5%
8 36499
 
4.6%

LENGUA
Text

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:22.983161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters799869
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2
ValueCountFrequency (%)
2 660550
82.6%
1 56588
 
7.1%
9 53612
 
6.7%
8 29119
 
3.6%
2025-08-22T17:40:23.048324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 660550
82.6%
1 56588
 
7.1%
9 53612
 
6.7%
8 29119
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 799869
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 660550
82.6%
1 56588
 
7.1%
9 53612
 
6.7%
8 29119
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 799869
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 660550
82.6%
1 56588
 
7.1%
9 53612
 
6.7%
8 29119
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 799869
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 660550
82.6%
1 56588
 
7.1%
9 53612
 
6.7%
8 29119
 
3.6%
Distinct63
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:23.105748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3199476
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st row8888
2nd row8888
3rd row8888
4th row8888
5th row8888
ValueCountFrequency (%)
8888 746696
93.4%
9000 11284
 
1.4%
0211 10410
 
1.3%
0602 5386
 
0.7%
0513 3560
 
0.4%
0516 3099
 
0.4%
0501 2690
 
0.3%
0701 2143
 
0.3%
0607 1950
 
0.2%
0606 1667
 
0.2%
Other values (53) 10984
 
1.4%
2025-08-22T17:40:23.279073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 2988036
93.4%
0 99154
 
3.1%
1 36723
 
1.1%
2 18315
 
0.6%
6 16470
 
0.5%
5 15418
 
0.5%
9 14265
 
0.4%
7 5697
 
0.2%
3 4027
 
0.1%
4 1371
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3199476
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 2988036
93.4%
0 99154
 
3.1%
1 36723
 
1.1%
2 18315
 
0.6%
6 16470
 
0.5%
5 15418
 
0.5%
9 14265
 
0.4%
7 5697
 
0.2%
3 4027
 
0.1%
4 1371
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3199476
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 2988036
93.4%
0 99154
 
3.1%
1 36723
 
1.1%
2 18315
 
0.6%
6 16470
 
0.5%
5 15418
 
0.5%
9 14265
 
0.4%
7 5697
 
0.2%
3 4027
 
0.1%
4 1371
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3199476
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 2988036
93.4%
0 99154
 
3.1%
1 36723
 
1.1%
2 18315
 
0.6%
6 16470
 
0.5%
5 15418
 
0.5%
9 14265
 
0.4%
7 5697
 
0.2%
3 4027
 
0.1%
4 1371
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:23.309738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters799869
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
1 783751
98.0%
9 10612
 
1.3%
2 5506
 
0.7%
2025-08-22T17:40:23.373247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 783751
98.0%
9 10612
 
1.3%
2 5506
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 799869
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 783751
98.0%
9 10612
 
1.3%
2 5506
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 799869
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 783751
98.0%
9 10612
 
1.3%
2 5506
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 799869
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 783751
98.0%
9 10612
 
1.3%
2 5506
 
0.7%
Distinct98
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:23.439545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2399607
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)< 0.1%

Sample

1st row998
2nd row998
3rd row998
4th row998
5th row998
ValueCountFrequency (%)
998 783751
98.0%
999 10612
 
1.3%
221 2302
 
0.3%
225 400
 
0.1%
213 344
 
< 0.1%
415 253
 
< 0.1%
229 252
 
< 0.1%
250 237
 
< 0.1%
220 175
 
< 0.1%
214 167
 
< 0.1%
Other values (88) 1376
 
0.2%
2025-08-22T17:40:23.550175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 1599974
66.7%
8 783867
32.7%
2 7991
 
0.3%
1 3570
 
0.1%
4 1131
 
< 0.1%
5 1111
 
< 0.1%
3 797
 
< 0.1%
0 730
 
< 0.1%
7 224
 
< 0.1%
6 212
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2399607
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 1599974
66.7%
8 783867
32.7%
2 7991
 
0.3%
1 3570
 
0.1%
4 1131
 
< 0.1%
5 1111
 
< 0.1%
3 797
 
< 0.1%
0 730
 
< 0.1%
7 224
 
< 0.1%
6 212
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2399607
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 1599974
66.7%
8 783867
32.7%
2 7991
 
0.3%
1 3570
 
0.1%
4 1131
 
< 0.1%
5 1111
 
< 0.1%
3 797
 
< 0.1%
0 730
 
< 0.1%
7 224
 
< 0.1%
6 212
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2399607
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 1599974
66.7%
8 783867
32.7%
2 7991
 
0.3%
1 3570
 
0.1%
4 1131
 
< 0.1%
5 1111
 
< 0.1%
3 797
 
< 0.1%
0 730
 
< 0.1%
7 224
 
< 0.1%
6 212
 
< 0.1%

EDAD
Real number (ℝ)

Distinct187
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4026.02961
Minimum1001
Maximum4998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:23.601212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile4020
Q14053
median4069
Q34082
95-th percentile4093
Maximum4998
Range3997
Interquartile range (IQR)29

Descriptive statistics

Standard deviation316.5098249
Coefficient of variation (CV)0.07861587112
Kurtosis51.09074819
Mean4026.02961
Median Absolute Deviation (MAD)14
Skewness-6.638437981
Sum3220296278
Variance100178.4693
MonotonicityNot monotonic
2025-08-22T17:40:23.659900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4082 16864
 
2.1%
4080 16673
 
2.1%
4078 16576
 
2.1%
4077 16516
 
2.1%
4079 16436
 
2.1%
4083 16405
 
2.1%
4075 16373
 
2.0%
4076 16329
 
2.0%
4081 16297
 
2.0%
4084 15961
 
2.0%
Other values (177) 635439
79.4%
ValueCountFrequency (%)
1001 566
0.1%
1002 307
< 0.1%
1003 203
 
< 0.1%
1004 139
 
< 0.1%
1005 111
 
< 0.1%
ValueCountFrequency (%)
4998 4490
0.6%
4120 3
 
< 0.1%
4119 2
 
< 0.1%
4118 4
 
< 0.1%
4117 2
 
< 0.1%

SEM_GEST
Real number (ℝ)

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.42576222
Minimum22
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:23.708599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile88
Q188
median88
Q388
95-th percentile88
Maximum99
Range77
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.922481847
Coefficient of variation (CV)0.06774298212
Kurtosis86.03030608
Mean87.42576222
Median Absolute Deviation (MAD)0
Skewness-9.238814326
Sum69929157
Variance35.07579122
MonotonicityNot monotonic
2025-08-22T17:40:23.755885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
88 787692
98.5%
99 3296
 
0.4%
38 659
 
0.1%
26 587
 
0.1%
28 585
 
0.1%
40 562
 
0.1%
39 560
 
0.1%
37 557
 
0.1%
30 499
 
0.1%
32 467
 
0.1%
Other values (13) 4405
 
0.6%
ValueCountFrequency (%)
22 231
 
< 0.1%
23 183
 
< 0.1%
24 352
< 0.1%
25 430
0.1%
26 587
0.1%
ValueCountFrequency (%)
99 3296
 
0.4%
88 787692
98.5%
42 90
 
< 0.1%
41 184
 
< 0.1%
40 562
 
0.1%

GRAMOS
Real number (ℝ)

Distinct1043
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8822.11249
Minimum600
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:23.810927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum600
5-th percentile8888
Q18888
median8888
Q38888
95-th percentile8888
Maximum9999
Range9399
Interquartile range (IQR)0

Descriptive statistics

Standard deviation713.5334817
Coefficient of variation (CV)0.08088011602
Kurtosis100.7367886
Mean8822.11249
Median Absolute Deviation (MAD)0
Skewness-9.991177027
Sum7056534295
Variance509130.0296
MonotonicityNot monotonic
2025-08-22T17:40:23.865662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8888 788688
98.6%
9999 3236
 
0.4%
600 569
 
0.1%
1000 97
 
< 0.1%
900 92
 
< 0.1%
3000 91
 
< 0.1%
800 90
 
< 0.1%
2000 89
 
< 0.1%
700 83
 
< 0.1%
1100 70
 
< 0.1%
Other values (1033) 6764
 
0.8%
ValueCountFrequency (%)
600 569
0.1%
605 6
 
< 0.1%
609 1
 
< 0.1%
610 16
 
< 0.1%
611 1
 
< 0.1%
ValueCountFrequency (%)
9999 3236
 
0.4%
8888 788688
98.6%
5624 1
 
< 0.1%
5480 1
 
< 0.1%
5460 1
 
< 0.1%

DIA_OCURR
Real number (ℝ)

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.7697448
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:23.914446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum99
Range98
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.17313062
Coefficient of variation (CV)0.5816917609
Kurtosis5.484796424
Mean15.7697448
Median Absolute Deviation (MAD)8
Skewness0.7471077231
Sum12613730
Variance84.14632537
MonotonicityNot monotonic
2025-08-22T17:40:23.962753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
20 26953
 
3.4%
18 26776
 
3.3%
2 26720
 
3.3%
3 26697
 
3.3%
17 26647
 
3.3%
22 26637
 
3.3%
21 26619
 
3.3%
19 26514
 
3.3%
12 26488
 
3.3%
1 26488
 
3.3%
Other values (22) 533330
66.7%
ValueCountFrequency (%)
1 26488
3.3%
2 26720
3.3%
3 26697
3.3%
4 26251
3.3%
5 26449
3.3%
ValueCountFrequency (%)
99 822
 
0.1%
31 15155
1.9%
30 23395
2.9%
29 23668
3.0%
28 25574
3.2%

MES_OCURR
Real number (ℝ)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.575170434
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:24.004701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q310
95-th percentile12
Maximum99
Range98
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.296289676
Coefficient of variation (CV)0.6534111502
Kurtosis148.8254041
Mean6.575170434
Median Absolute Deviation (MAD)3
Skewness6.988132518
Sum5259275
Variance18.45810498
MonotonicityNot monotonic
2025-08-22T17:40:24.043597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 76925
9.6%
12 76389
9.6%
6 69261
8.7%
11 66517
8.3%
3 64927
8.1%
5 64917
8.1%
7 64405
8.1%
8 64379
8.0%
10 64250
8.0%
2 62895
7.9%
Other values (3) 125004
15.6%
ValueCountFrequency (%)
1 76925
9.6%
2 62895
7.9%
3 64927
8.1%
4 62178
7.8%
5 64917
8.1%
ValueCountFrequency (%)
99 564
 
0.1%
12 76389
9.6%
11 66517
8.3%
10 64250
8.0%
9 62262
7.8%

ANIO_OCUR
Real number (ℝ)

Skewed 

Distinct61
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2025.811725
Minimum1900
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:24.092581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile2023
Q12023
median2023
Q32023
95-th percentile2023
Maximum9999
Range8099
Interquartile range (IQR)0

Descriptive statistics

Standard deviation151.0590568
Coefficient of variation (CV)0.07456717469
Kurtosis2781.888638
Mean2025.811725
Median Absolute Deviation (MAD)0
Skewness52.7618851
Sum1620383999
Variance22818.83865
MonotonicityNot monotonic
2025-08-22T17:40:24.151257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2023 779239
97.4%
2022 16847
 
2.1%
2021 1108
 
0.1%
2020 734
 
0.1%
2019 347
 
< 0.1%
9999 287
 
< 0.1%
2018 226
 
< 0.1%
2017 168
 
< 0.1%
2016 118
 
< 0.1%
2015 83
 
< 0.1%
Other values (51) 712
 
0.1%
ValueCountFrequency (%)
1900 1
 
< 0.1%
1961 1
 
< 0.1%
1963 4
< 0.1%
1964 4
< 0.1%
1965 1
 
< 0.1%
ValueCountFrequency (%)
9999 287
 
< 0.1%
2023 779239
97.4%
2022 16847
 
2.1%
2021 1108
 
0.1%
2020 734
 
0.1%

DIA_REGIS
Real number (ℝ)

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.64103122
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:24.203093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median17
Q325
95-th percentile99
Maximum99
Range98
Interquartile range (IQR)16

Descriptive statistics

Standard deviation21.32480553
Coefficient of variation (CV)1.033126945
Kurtosis7.808099928
Mean20.64103122
Median Absolute Deviation (MAD)8
Skewness2.794702211
Sum16510121
Variance454.7473308
MonotonicityNot monotonic
2025-08-22T17:40:24.251815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
99 46980
 
5.9%
13 27223
 
3.4%
27 26658
 
3.3%
21 26553
 
3.3%
17 26063
 
3.3%
3 25880
 
3.2%
24 25582
 
3.2%
10 25520
 
3.2%
6 25448
 
3.2%
11 25272
 
3.2%
Other values (22) 518690
64.8%
ValueCountFrequency (%)
1 22566
2.8%
2 23428
2.9%
3 25880
3.2%
4 24929
3.1%
5 23436
2.9%
ValueCountFrequency (%)
99 46980
5.9%
31 14663
 
1.8%
30 21536
2.7%
29 20720
2.6%
28 24811
3.1%

MES_REGIS
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.468629238
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:24.293255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.519103362
Coefficient of variation (CV)0.5440261349
Kurtosis-1.233698296
Mean6.468629238
Median Absolute Deviation (MAD)3
Skewness0.005177667072
Sum5174056
Variance12.38408847
MonotonicityNot monotonic
2025-08-22T17:40:24.331499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 78539
9.8%
12 72837
9.1%
6 69330
8.7%
3 66850
8.4%
5 66065
8.3%
11 65979
8.2%
8 65956
8.2%
10 64903
8.1%
7 63282
7.9%
2 63125
7.9%
Other values (2) 123003
15.4%
ValueCountFrequency (%)
1 78539
9.8%
2 63125
7.9%
3 66850
8.4%
4 60816
7.6%
5 66065
8.3%
ValueCountFrequency (%)
12 72837
9.1%
11 65979
8.2%
10 64903
8.1%
9 62187
7.8%
8 65956
8.2%

ANIO_REGIS
Real number (ℝ)

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2023
Minimum2023
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:24.364547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2023
5-th percentile2023
Q12023
median2023
Q32023
95-th percentile2023
Maximum2023
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean2023
Median Absolute Deviation (MAD)0
Skewness0
Sum1618134987
Variance0
MonotonicityIncreasing
2025-08-22T17:40:24.396076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
2023 799869
100.0%
ValueCountFrequency (%)
2023 799869
100.0%
ValueCountFrequency (%)
2023 799869
100.0%

DIA_NACIM
Real number (ℝ)

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.635129
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:24.437481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum99
Range98
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.89333603
Coefficient of variation (CV)0.7750667896
Kurtosis19.19257026
Mean16.635129
Median Absolute Deviation (MAD)8
Skewness3.298299005
Sum13305924
Variance166.238114
MonotonicityNot monotonic
2025-08-22T17:40:24.486734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
15 32486
 
4.1%
1 30446
 
3.8%
20 30425
 
3.8%
10 29921
 
3.7%
25 28025
 
3.5%
12 27429
 
3.4%
8 27139
 
3.4%
2 26994
 
3.4%
24 26733
 
3.3%
28 26446
 
3.3%
Other values (22) 513825
64.2%
ValueCountFrequency (%)
1 30446
3.8%
2 26994
3.4%
3 25321
3.2%
4 25922
3.2%
5 26312
3.3%
ValueCountFrequency (%)
99 10464
 
1.3%
31 12970
1.6%
30 23064
2.9%
29 23675
3.0%
28 26446
3.3%

MES_NACIM
Real number (ℝ)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.707659629
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:24.528226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum99
Range98
Interquartile range (IQR)6

Descriptive statistics

Standard deviation11.04062029
Coefficient of variation (CV)1.432421879
Kurtosis58.06382855
Mean7.707659629
Median Absolute Deviation (MAD)3
Skewness7.349271161
Sum6165118
Variance121.8952963
MonotonicityNot monotonic
2025-08-22T17:40:24.567251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
8 69074
8.6%
7 67879
8.5%
5 67767
8.5%
6 67546
8.4%
1 67330
8.4%
3 66894
8.4%
12 66380
8.3%
10 65276
8.2%
9 64554
8.1%
4 63212
7.9%
Other values (3) 133957
16.7%
ValueCountFrequency (%)
1 67330
8.4%
2 61167
7.6%
3 66894
8.4%
4 63212
7.9%
5 67767
8.5%
ValueCountFrequency (%)
99 10444
 
1.3%
12 66380
8.3%
11 62346
7.8%
10 65276
8.2%
9 64554
8.1%

ANIO_NACIM
Real number (ℝ)

Distinct126
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2060.659277
Minimum1897
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:24.616206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1897
5-th percentile1930
Q11941
median1954
Q31971
95-th percentile2007
Maximum9999
Range8102
Interquartile range (IQR)30

Descriptive statistics

Standard deviation903.4257617
Coefficient of variation (CV)0.4384158856
Kurtosis73.17781308
Mean2060.659277
Median Absolute Deviation (MAD)14
Skewness8.667731948
Sum1648257475
Variance816178.107
MonotonicityNot monotonic
2025-08-22T17:40:24.673836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1940 17465
 
2.2%
1945 16778
 
2.1%
1942 16735
 
2.1%
1943 16418
 
2.1%
1947 16414
 
2.1%
2023 16342
 
2.0%
1944 16267
 
2.0%
1946 16165
 
2.0%
1948 15993
 
2.0%
1941 15883
 
2.0%
Other values (116) 635409
79.4%
ValueCountFrequency (%)
1897 1
 
< 0.1%
1900 1
 
< 0.1%
1901 3
< 0.1%
1902 5
< 0.1%
1903 2
 
< 0.1%
ValueCountFrequency (%)
9999 10221
1.3%
2023 16342
2.0%
2022 3942
 
0.5%
2021 1304
 
0.2%
2020 902
 
0.1%

COND_ACT
Real number (ℝ)

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.14363227
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:24.715657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.977325092
Coefficient of variation (CV)0.922418047
Kurtosis6.63235194
Mean2.14363227
Median Absolute Deviation (MAD)0
Skewness2.801919884
Sum1714625
Variance3.909814519
MonotonicityNot monotonic
2025-08-22T17:40:24.837460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
2 425630
53.2%
1 310149
38.8%
9 40496
 
5.1%
8 23594
 
2.9%
ValueCountFrequency (%)
1 310149
38.8%
2 425630
53.2%
8 23594
 
2.9%
9 40496
 
5.1%
ValueCountFrequency (%)
9 40496
 
5.1%
8 23594
 
2.9%
2 425630
53.2%
1 310149
38.8%
Distinct56
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:24.905282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2399607
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row023
2nd row110
3rd row110
4th row110
5th row051
ValueCountFrequency (%)
110 425630
53.2%
061 75062
 
9.4%
041 41174
 
5.1%
999 40496
 
5.1%
998 39777
 
5.0%
997 23594
 
2.9%
071 21557
 
2.7%
083 17112
 
2.1%
098 15485
 
1.9%
079 14479
 
1.8%
Other values (46) 85503
 
10.7%
2025-08-22T17:40:25.016324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1007766
42.0%
0 696053
29.0%
9 294475
 
12.3%
6 90519
 
3.8%
8 77083
 
3.2%
7 74503
 
3.1%
2 51316
 
2.1%
4 48037
 
2.0%
3 43778
 
1.8%
5 16077
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2399607
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1007766
42.0%
0 696053
29.0%
9 294475
 
12.3%
6 90519
 
3.8%
8 77083
 
3.2%
7 74503
 
3.1%
2 51316
 
2.1%
4 48037
 
2.0%
3 43778
 
1.8%
5 16077
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2399607
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1007766
42.0%
0 696053
29.0%
9 294475
 
12.3%
6 90519
 
3.8%
8 77083
 
3.2%
7 74503
 
3.1%
2 51316
 
2.1%
4 48037
 
2.0%
3 43778
 
1.8%
5 16077
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2399607
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1007766
42.0%
0 696053
29.0%
9 294475
 
12.3%
6 90519
 
3.8%
8 77083
 
3.2%
7 74503
 
3.1%
2 51316
 
2.1%
4 48037
 
2.0%
3 43778
 
1.8%
5 16077
 
0.7%

ESCOLARIDA
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.657989
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:25.060178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q37
95-th percentile88
Maximum99
Range98
Interquartile range (IQR)4

Descriptive statistics

Standard deviation22.92526971
Coefficient of variation (CV)2.150993937
Kurtosis9.595970167
Mean10.657989
Median Absolute Deviation (MAD)2
Skewness3.37235598
Sum8524995
Variance525.5679913
MonotonicityNot monotonic
2025-08-22T17:40:25.100992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
4 170988
21.4%
3 166561
20.8%
1 126459
15.8%
6 106041
13.3%
9 70781
8.8%
8 61179
 
7.6%
99 32611
 
4.1%
88 22207
 
2.8%
5 20182
 
2.5%
7 16327
 
2.0%
Other values (2) 6533
 
0.8%
ValueCountFrequency (%)
1 126459
15.8%
2 1380
 
0.2%
3 166561
20.8%
4 170988
21.4%
5 20182
 
2.5%
ValueCountFrequency (%)
99 32611
4.1%
88 22207
 
2.8%
10 5153
 
0.6%
9 70781
8.8%
8 61179
7.6%

EDO_CIVIL
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.80750848
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:25.138559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.199056274
Coefficient of variation (CV)0.577557814
Kurtosis-0.02585246569
Mean3.80750848
Median Absolute Deviation (MAD)1
Skewness0.5527557413
Sum3045508
Variance4.835848497
MonotonicityNot monotonic
2025-08-22T17:40:25.176252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5 265146
33.1%
1 192552
24.1%
3 171938
21.5%
4 66909
 
8.4%
9 47086
 
5.9%
8 27127
 
3.4%
2 17920
 
2.2%
6 11191
 
1.4%
ValueCountFrequency (%)
1 192552
24.1%
2 17920
 
2.2%
3 171938
21.5%
4 66909
 
8.4%
5 265146
33.1%
ValueCountFrequency (%)
9 47086
 
5.9%
8 27127
 
3.4%
6 11191
 
1.4%
5 265146
33.1%
4 66909
 
8.4%

TIPO_DEFUN
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.772481494
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:25.211591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median4
Q34
95-th percentile4
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8072964702
Coefficient of variation (CV)0.213996138
Kurtosis10.41337388
Mean3.772481494
Median Absolute Deviation (MAD)0
Skewness-1.613910454
Sum3017491
Variance0.6517275909
MonotonicityNot monotonic
2025-08-22T17:40:25.248821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 715751
89.5%
1 40282
 
5.0%
2 32253
 
4.0%
3 9072
 
1.1%
9 2482
 
0.3%
5 29
 
< 0.1%
ValueCountFrequency (%)
1 40282
 
5.0%
2 32253
 
4.0%
3 9072
 
1.1%
4 715751
89.5%
5 29
 
< 0.1%
ValueCountFrequency (%)
9 2482
 
0.3%
5 29
 
< 0.1%
4 715751
89.5%
3 9072
 
1.1%
2 32253
 
4.0%

OCURR_TRAB
Real number (ℝ)

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.651390415
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:25.286402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median8
Q38
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.522682278
Coefficient of variation (CV)0.1990072648
Kurtosis10.71131892
Mean7.651390415
Median Absolute Deviation (MAD)0
Skewness-3.504374782
Sum6120110
Variance2.31856132
MonotonicityNot monotonic
2025-08-22T17:40:25.322823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
8 717363
89.7%
2 42692
 
5.3%
9 32001
 
4.0%
1 7813
 
1.0%
ValueCountFrequency (%)
1 7813
 
1.0%
2 42692
 
5.3%
8 717363
89.7%
9 32001
 
4.0%
ValueCountFrequency (%)
9 32001
 
4.0%
8 717363
89.7%
2 42692
 
5.3%
1 7813
 
1.0%

LUGAR_OCUR
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.27852061
Minimum0
Maximum88
Zeros14218
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:25.362051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q188
median88
Q388
95-th percentile88
Maximum88
Range88
Interquartile range (IQR)0

Descriptive statistics

Standard deviation25.46094636
Coefficient of variation (CV)0.3211581922
Kurtosis4.683966933
Mean79.27852061
Median Absolute Deviation (MAD)0
Skewness-2.582026946
Sum63412431
Variance648.2597893
MonotonicityNot monotonic
2025-08-22T17:40:25.399414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
88 715751
89.5%
4 36296
 
4.5%
9 22481
 
2.8%
0 14218
 
1.8%
8 7329
 
0.9%
7 1307
 
0.2%
5 1296
 
0.2%
6 572
 
0.1%
2 244
 
< 0.1%
1 238
 
< 0.1%
ValueCountFrequency (%)
0 14218
 
1.8%
1 238
 
< 0.1%
2 244
 
< 0.1%
3 137
 
< 0.1%
4 36296
4.5%
ValueCountFrequency (%)
88 715751
89.5%
9 22481
 
2.8%
8 7329
 
0.9%
7 1307
 
0.2%
6 572
 
0.1%

PAR_AGRE
Real number (ℝ)

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.42918278
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:25.442759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile88
Q188
median88
Q388
95-th percentile88
Maximum99
Range98
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.293832596
Coefficient of variation (CV)0.02593976925
Kurtosis139.0777799
Mean88.42918278
Median Absolute Deviation (MAD)0
Skewness0.2760747408
Sum70731762
Variance5.26166798
MonotonicityNot monotonic
2025-08-22T17:40:25.486022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
88 767616
96.0%
99 32001
 
4.0%
72 122
 
< 0.1%
71 29
 
< 0.1%
45 16
 
< 0.1%
3 15
 
< 0.1%
1 10
 
< 0.1%
5 9
 
< 0.1%
11 8
 
< 0.1%
51 8
 
< 0.1%
Other values (15) 35
 
< 0.1%
ValueCountFrequency (%)
1 10
< 0.1%
2 3
 
< 0.1%
3 15
< 0.1%
5 9
< 0.1%
11 8
< 0.1%
ValueCountFrequency (%)
99 32001
 
4.0%
88 767616
96.0%
72 122
 
< 0.1%
71 29
 
< 0.1%
70 3
 
< 0.1%

VIO_FAMI
Real number (ℝ)

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.039076399
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:25.522280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q18
median8
Q38
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2141981686
Coefficient of variation (CV)0.02664462408
Kurtosis173.4210023
Mean8.039076399
Median Absolute Deviation (MAD)0
Skewness-1.428599592
Sum6430208
Variance0.04588085544
MonotonicityNot monotonic
2025-08-22T17:40:25.556219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
8 767617
96.0%
9 32122
 
4.0%
1 86
 
< 0.1%
2 44
 
< 0.1%
ValueCountFrequency (%)
1 86
 
< 0.1%
2 44
 
< 0.1%
8 767617
96.0%
9 32122
 
4.0%
ValueCountFrequency (%)
9 32122
 
4.0%
8 767617
96.0%
2 44
 
< 0.1%
1 86
 
< 0.1%

ASIST_MEDI
Real number (ℝ)

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.648603709
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:25.589959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.94926308
Coefficient of variation (CV)1.182372131
Kurtosis9.91286061
Mean1.648603709
Median Absolute Deviation (MAD)0
Skewness3.392686448
Sum1318667
Variance3.799626555
MonotonicityNot monotonic
2025-08-22T17:40:25.626139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
1 638183
79.8%
2 110670
 
13.8%
9 51016
 
6.4%
ValueCountFrequency (%)
1 638183
79.8%
2 110670
 
13.8%
9 51016
 
6.4%
ValueCountFrequency (%)
9 51016
 
6.4%
2 110670
 
13.8%
1 638183
79.8%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:25.655750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters799869
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9
2nd row9
3rd row9
4th row9
5th row9
ValueCountFrequency (%)
9 713562
89.2%
1 47280
 
5.9%
8 36499
 
4.6%
2 2528
 
0.3%
2025-08-22T17:40:25.721400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 713562
89.2%
1 47280
 
5.9%
8 36499
 
4.6%
2 2528
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 799869
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 713562
89.2%
1 47280
 
5.9%
8 36499
 
4.6%
2 2528
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 799869
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 713562
89.2%
1 47280
 
5.9%
8 36499
 
4.6%
2 2528
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 799869
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 713562
89.2%
1 47280
 
5.9%
8 36499
 
4.6%
2 2528
 
0.3%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:25.747139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters799869
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2
ValueCountFrequency (%)
2 685538
85.7%
1 77832
 
9.7%
8 36499
 
4.6%
2025-08-22T17:40:25.812575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 685538
85.7%
1 77832
 
9.7%
8 36499
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 799869
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 685538
85.7%
1 77832
 
9.7%
8 36499
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 799869
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 685538
85.7%
1 77832
 
9.7%
8 36499
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 799869
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 685538
85.7%
1 77832
 
9.7%
8 36499
 
4.6%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:25.841403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters799869
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row9
ValueCountFrequency (%)
2 567078
70.9%
9 141059
 
17.6%
1 91732
 
11.5%
2025-08-22T17:40:25.906606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 567078
70.9%
9 141059
 
17.6%
1 91732
 
11.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 799869
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 567078
70.9%
9 141059
 
17.6%
1 91732
 
11.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 799869
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 567078
70.9%
9 141059
 
17.6%
1 91732
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 799869
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 567078
70.9%
9 141059
 
17.6%
1 91732
 
11.5%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:25.932475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters799869
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8
ValueCountFrequency (%)
8 710666
88.8%
1 78757
 
9.8%
9 9276
 
1.2%
2 1170
 
0.1%
2025-08-22T17:40:25.997133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 710666
88.8%
1 78757
 
9.8%
9 9276
 
1.2%
2 1170
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 799869
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 710666
88.8%
1 78757
 
9.8%
9 9276
 
1.2%
2 1170
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 799869
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 710666
88.8%
1 78757
 
9.8%
9 9276
 
1.2%
2 1170
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 799869
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 710666
88.8%
1 78757
 
9.8%
9 9276
 
1.2%
2 1170
 
0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:26.025847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters799869
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9
2nd row2
3rd row2
4th row2
5th row9
ValueCountFrequency (%)
2 432005
54.0%
9 319412
39.9%
8 36499
 
4.6%
1 11953
 
1.5%
2025-08-22T17:40:26.091830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 432005
54.0%
9 319412
39.9%
8 36499
 
4.6%
1 11953
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 799869
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 432005
54.0%
9 319412
39.9%
8 36499
 
4.6%
1 11953
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 799869
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 432005
54.0%
9 319412
39.9%
8 36499
 
4.6%
1 11953
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 799869
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 432005
54.0%
9 319412
39.9%
8 36499
 
4.6%
1 11953
 
1.5%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:26.118921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters799869
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8
ValueCountFrequency (%)
8 787916
98.5%
2 9888
 
1.2%
9 1593
 
0.2%
1 472
 
0.1%
2025-08-22T17:40:26.185700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 787916
98.5%
2 9888
 
1.2%
9 1593
 
0.2%
1 472
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 799869
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 787916
98.5%
2 9888
 
1.2%
9 1593
 
0.2%
1 472
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 799869
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 787916
98.5%
2 9888
 
1.2%
9 1593
 
0.2%
1 472
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 799869
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 787916
98.5%
2 9888
 
1.2%
9 1593
 
0.2%
1 472
 
0.1%

SITIO_OCUR
Real number (ℝ)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.677633462
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:26.222070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median11
Q311
95-th percentile12
Maximum99
Range98
Interquartile range (IQR)8

Descriptive statistics

Standard deviation13.71504202
Coefficient of variation (CV)1.417189654
Kurtosis34.85326264
Mean9.677633462
Median Absolute Deviation (MAD)1
Skewness5.751226399
Sum7740839
Variance188.1023775
MonotonicityNot monotonic
2025-08-22T17:40:26.261983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
11 374623
46.8%
3 144835
 
18.1%
1 99391
 
12.4%
12 42799
 
5.4%
9 35299
 
4.4%
10 34489
 
4.3%
4 26434
 
3.3%
99 16815
 
2.1%
8 10869
 
1.4%
2 7833
 
1.0%
Other values (3) 6482
 
0.8%
ValueCountFrequency (%)
1 99391
12.4%
2 7833
 
1.0%
3 144835
18.1%
4 26434
 
3.3%
5 2841
 
0.4%
ValueCountFrequency (%)
99 16815
 
2.1%
12 42799
 
5.4%
11 374623
46.8%
10 34489
 
4.3%
9 35299
 
4.4%

COND_CERT
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.862732522
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:26.297308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.657363556
Coefficient of variation (CV)0.5789446072
Kurtosis6.52699289
Mean2.862732522
Median Absolute Deviation (MAD)0
Skewness2.318044048
Sum2289811
Variance2.746853956
MonotonicityNot monotonic
2025-08-22T17:40:26.334785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 490025
61.3%
1 140961
 
17.6%
2 109474
 
13.7%
9 32670
 
4.1%
8 13291
 
1.7%
4 7771
 
1.0%
5 5677
 
0.7%
ValueCountFrequency (%)
1 140961
 
17.6%
2 109474
 
13.7%
3 490025
61.3%
4 7771
 
1.0%
5 5677
 
0.7%
ValueCountFrequency (%)
9 32670
 
4.1%
8 13291
 
1.7%
5 5677
 
0.7%
4 7771
 
1.0%
3 490025
61.3%

DERECHOHAB
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.09391538
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:26.372774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile99
Maximum99
Range98
Interquartile range (IQR)2

Descriptive statistics

Standard deviation33.17965688
Coefficient of variation (CV)2.198214052
Kurtosis2.542241696
Mean15.09391538
Median Absolute Deviation (MAD)1
Skewness2.1272579
Sum12073155
Variance1100.889631
MonotonicityNot monotonic
2025-08-22T17:40:26.413211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 312459
39.1%
2 277480
34.7%
99 107955
 
13.5%
3 55765
 
7.0%
8 20451
 
2.6%
9 11794
 
1.5%
4 5937
 
0.7%
5 2880
 
0.4%
10 2706
 
0.3%
7 1282
 
0.2%
ValueCountFrequency (%)
1 312459
39.1%
2 277480
34.7%
3 55765
 
7.0%
4 5937
 
0.7%
5 2880
 
0.4%
ValueCountFrequency (%)
99 107955
13.5%
10 2706
 
0.3%
9 11794
 
1.5%
8 20451
 
2.6%
7 1282
 
0.2%

EMBARAZO
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.265457219
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:26.448853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q18
median8
Q39
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.854270547
Coefficient of variation (CV)0.1033543002
Kurtosis10.87578953
Mean8.265457219
Median Absolute Deviation (MAD)0
Skewness-2.63833366
Sum6611283
Variance0.7297781675
MonotonicityNot monotonic
2025-08-22T17:40:26.485419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
8 455674
57.0%
9 312396
39.1%
5 29960
 
3.7%
2 853
 
0.1%
3 391
 
< 0.1%
4 351
 
< 0.1%
1 244
 
< 0.1%
ValueCountFrequency (%)
1 244
 
< 0.1%
2 853
 
0.1%
3 391
 
< 0.1%
4 351
 
< 0.1%
5 29960
3.7%
ValueCountFrequency (%)
9 312396
39.1%
8 455674
57.0%
5 29960
 
3.7%
4 351
 
< 0.1%
3 391
 
< 0.1%

REL_EMBA
Real number (ℝ)

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.273914854
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:26.519253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q18
median8
Q39
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.068247972
Coefficient of variation (CV)0.1291103415
Kurtosis24.05494177
Mean8.273914854
Median Absolute Deviation (MAD)0
Skewness-4.448453535
Sum6618048
Variance1.141153729
MonotonicityNot monotonic
2025-08-22T17:40:26.552973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
8 455674
57.0%
9 326379
40.8%
2 17429
 
2.2%
1 387
 
< 0.1%
ValueCountFrequency (%)
1 387
 
< 0.1%
2 17429
 
2.2%
8 455674
57.0%
9 326379
40.8%
ValueCountFrequency (%)
9 326379
40.8%
8 455674
57.0%
2 17429
 
2.2%
1 387
 
< 0.1%

HORAS
Real number (ℝ)

Zeros 

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.97266177
Minimum0
Maximum99
Zeros31107
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:26.594822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median12
Q318
95-th percentile23
Maximum99
Range99
Interquartile range (IQR)12

Descriptive statistics

Standard deviation12.27431363
Coefficient of variation (CV)0.9461677063
Kurtosis31.19191199
Mean12.97266177
Median Absolute Deviation (MAD)6
Skewness4.750501737
Sum10376430
Variance150.6587752
MonotonicityNot monotonic
2025-08-22T17:40:26.640533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
10 36428
 
4.6%
12 35853
 
4.5%
16 35751
 
4.5%
18 35709
 
4.5%
11 35704
 
4.5%
17 35460
 
4.4%
15 35209
 
4.4%
14 34928
 
4.4%
13 34605
 
4.3%
6 34501
 
4.3%
Other values (15) 445721
55.7%
ValueCountFrequency (%)
0 31107
3.9%
1 30054
3.8%
2 27143
3.4%
3 28224
3.5%
4 29138
3.6%
ValueCountFrequency (%)
99 11271
 
1.4%
23 30301
3.8%
22 30943
3.9%
21 31364
3.9%
20 34241
4.3%

MINUTOS
Real number (ℝ)

Zeros 

Distinct61
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.13589225
Minimum0
Maximum99
Zeros198635
Zeros (%)24.8%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:26.693507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median23
Q337
95-th percentile54
Maximum99
Range99
Interquartile range (IQR)36

Descriptive statistics

Standard deviation20.04717594
Coefficient of variation (CV)0.8664967713
Kurtosis1.025559993
Mean23.13589225
Median Absolute Deviation (MAD)17
Skewness0.7833623883
Sum18505683
Variance401.889263
MonotonicityNot monotonic
2025-08-22T17:40:26.749661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 198635
24.8%
30 128787
16.1%
20 41616
 
5.2%
40 40672
 
5.1%
15 36836
 
4.6%
50 34980
 
4.4%
45 33003
 
4.1%
10 32216
 
4.0%
5 18765
 
2.3%
35 15615
 
2.0%
Other values (51) 218744
27.3%
ValueCountFrequency (%)
0 198635
24.8%
1 4496
 
0.6%
2 4925
 
0.6%
3 4338
 
0.5%
4 3882
 
0.5%
ValueCountFrequency (%)
99 11271
1.4%
59 2652
 
0.3%
58 4520
0.6%
57 3762
 
0.5%
56 3587
 
0.4%

CAPITULO
Real number (ℝ)

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.017967942
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:26.795583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median9
Q311
95-th percentile20
Maximum22
Range21
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.461022154
Coefficient of variation (CV)0.6055712538
Kurtosis-0.3042958309
Mean9.017967942
Median Absolute Deviation (MAD)5
Skewness0.5954946374
Sum7213193
Variance29.82276296
MonotonicityNot monotonic
2025-08-22T17:40:26.927519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
9 227912
28.5%
4 127068
15.9%
2 100216
12.5%
20 84118
 
10.5%
11 74420
 
9.3%
10 66821
 
8.4%
14 30407
 
3.8%
1 21242
 
2.7%
6 14133
 
1.8%
16 10169
 
1.3%
Other values (10) 43363
 
5.4%
ValueCountFrequency (%)
1 21242
 
2.7%
2 100216
12.5%
3 4978
 
0.6%
4 127068
15.9%
5 5115
 
0.6%
ValueCountFrequency (%)
22 4687
 
0.6%
20 84118
10.5%
18 9783
 
1.2%
17 7400
 
0.9%
16 10169
 
1.3%

GRUPO
Real number (ℝ)

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.624624782
Minimum1
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:26.969329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median4
Q38
95-th percentile26
Maximum34
Range33
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.464396365
Coefficient of variation (CV)0.9758132086
Kurtosis3.911207152
Mean6.624624782
Median Absolute Deviation (MAD)2
Skewness2.121222015
Sum5298832
Variance41.78842036
MonotonicityNot monotonic
2025-08-22T17:40:27.020253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
2 189679
23.7%
4 163387
20.4%
8 70341
 
8.8%
3 64199
 
8.0%
7 54793
 
6.9%
6 39599
 
5.0%
5 39109
 
4.9%
27 32252
 
4.0%
9 29126
 
3.6%
1 25027
 
3.1%
Other values (24) 92357
11.5%
ValueCountFrequency (%)
1 25027
 
3.1%
2 189679
23.7%
3 64199
 
8.0%
4 163387
20.4%
5 39109
 
4.9%
ValueCountFrequency (%)
34 487
0.1%
33 393
< 0.1%
32 34
 
< 0.1%
31 13
 
< 0.1%
30 187
 
< 0.1%

LISTA1
Text

Distinct84
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:27.116585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.982420871
Min length0

Characters and Unicode

Total characters2385546
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row074
2nd row077
3rd row076
4th row053
5th row080
ValueCountFrequency (%)
067 142764
18.0%
052 110059
 
13.8%
080 40052
 
5.0%
069 34428
 
4.3%
074 33454
 
4.2%
102 32252
 
4.1%
081 31366
 
3.9%
066 27651
 
3.5%
086 26562
 
3.3%
076 21584
 
2.7%
Other values (73) 295010
37.1%
2025-08-22T17:40:27.269411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 866701
36.3%
6 359107
15.1%
7 247675
 
10.4%
2 188224
 
7.9%
5 148267
 
6.2%
8 135659
 
5.7%
1 135065
 
5.7%
4 112520
 
4.7%
9 102472
 
4.3%
3 89856
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2385546
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 866701
36.3%
6 359107
15.1%
7 247675
 
10.4%
2 188224
 
7.9%
5 148267
 
6.2%
8 135659
 
5.7%
1 135065
 
5.7%
4 112520
 
4.7%
9 102472
 
4.3%
3 89856
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2385546
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 866701
36.3%
6 359107
15.1%
7 247675
 
10.4%
2 188224
 
7.9%
5 148267
 
6.2%
8 135659
 
5.7%
1 135065
 
5.7%
4 112520
 
4.7%
9 102472
 
4.3%
3 89856
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2385546
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 866701
36.3%
6 359107
15.1%
7 247675
 
10.4%
2 188224
 
7.9%
5 148267
 
6.2%
8 135659
 
5.7%
1 135065
 
5.7%
4 112520
 
4.7%
9 102472
 
4.3%
3 89856
 
3.8%
Distinct56
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:27.352994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2399607
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 33
2nd row 33
3rd row 33
4th row 21
5th row 35
ValueCountFrequency (%)
28 142764
17.8%
20 121116
15.1%
35 74239
 
9.3%
33 66555
 
8.3%
30 34428
 
4.3%
e55 32252
 
4.0%
09 29993
 
3.7%
38 28634
 
3.6%
27 27651
 
3.5%
12 22640
 
2.8%
Other values (46) 219597
27.5%
2025-08-22T17:40:27.472899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
715751
29.8%
2 367066
15.3%
3 318968
13.3%
0 222650
 
9.3%
8 190741
 
7.9%
5 174264
 
7.3%
1 118051
 
4.9%
E 84118
 
3.5%
9 71851
 
3.0%
4 64646
 
2.7%
Other values (2) 71501
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2399607
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
715751
29.8%
2 367066
15.3%
3 318968
13.3%
0 222650
 
9.3%
8 190741
 
7.9%
5 174264
 
7.3%
1 118051
 
4.9%
E 84118
 
3.5%
9 71851
 
3.0%
4 64646
 
2.7%
Other values (2) 71501
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2399607
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
715751
29.8%
2 367066
15.3%
3 318968
13.3%
0 222650
 
9.3%
8 190741
 
7.9%
5 174264
 
7.3%
1 118051
 
4.9%
E 84118
 
3.5%
9 71851
 
3.0%
4 64646
 
2.7%
Other values (2) 71501
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2399607
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
715751
29.8%
2 367066
15.3%
3 318968
13.3%
0 222650
 
9.3%
8 190741
 
7.9%
5 174264
 
7.3%
1 118051
 
4.9%
E 84118
 
3.5%
9 71851
 
3.0%
4 64646
 
2.7%
Other values (2) 71501
 
3.0%

AREA_UR
Real number (ℝ)

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.311683538
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:27.512238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.033430209
Coefficient of variation (CV)0.7878655015
Kurtosis43.73016768
Mean1.311683538
Median Absolute Deviation (MAD)0
Skewness6.309593008
Sum1049175
Variance1.067977996
MonotonicityNot monotonic
2025-08-22T17:40:27.547344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
1 635893
79.5%
2 151786
 
19.0%
9 12190
 
1.5%
ValueCountFrequency (%)
1 635893
79.5%
2 151786
 
19.0%
9 12190
 
1.5%
ValueCountFrequency (%)
9 12190
 
1.5%
2 151786
 
19.0%
1 635893
79.5%
Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:27.609962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1599738
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row19
2nd row20
3rd row18
4th row23
5th row14
ValueCountFrequency (%)
20 82230
10.3%
21 82200
10.3%
19 77672
9.7%
22 73394
9.2%
18 72848
9.1%
17 64494
 
8.1%
16 53333
 
6.7%
23 48321
 
6.0%
15 43270
 
5.4%
14 34552
 
4.3%
Other values (20) 167555
20.9%
2025-08-22T17:40:27.714354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 553007
34.6%
2 407657
25.5%
0 157310
 
9.8%
9 92084
 
5.8%
8 82244
 
5.1%
3 80033
 
5.0%
7 67878
 
4.2%
6 56335
 
3.5%
4 54839
 
3.4%
5 48351
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1599738
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 553007
34.6%
2 407657
25.5%
0 157310
 
9.8%
9 92084
 
5.8%
8 82244
 
5.1%
3 80033
 
5.0%
7 67878
 
4.2%
6 56335
 
3.5%
4 54839
 
3.4%
5 48351
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1599738
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 553007
34.6%
2 407657
25.5%
0 157310
 
9.8%
9 92084
 
5.8%
8 82244
 
5.1%
3 80033
 
5.0%
7 67878
 
4.2%
6 56335
 
3.5%
4 54839
 
3.4%
5 48351
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1599738
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 553007
34.6%
2 407657
25.5%
0 157310
 
9.8%
9 92084
 
5.8%
8 82244
 
5.1%
3 80033
 
5.0%
7 67878
 
4.2%
6 56335
 
3.5%
4 54839
 
3.4%
5 48351
 
3.0%

COMPLICARO
Real number (ℝ)

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.280105867
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:27.750062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q18
median8
Q39
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.052216105
Coefficient of variation (CV)0.127077615
Kurtosis24.77054595
Mean8.280105867
Median Absolute Deviation (MAD)0
Skewness-4.489070206
Sum6623000
Variance1.107158732
MonotonicityNot monotonic
2025-08-22T17:40:27.785818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
8 455674
57.0%
9 327092
40.9%
2 16677
 
2.1%
1 426
 
0.1%
ValueCountFrequency (%)
1 426
 
0.1%
2 16677
 
2.1%
8 455674
57.0%
9 327092
40.9%
ValueCountFrequency (%)
9 327092
40.9%
8 455674
57.0%
2 16677
 
2.1%
1 426
 
0.1%

DIA_CERT
Real number (ℝ)

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.16452444
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:27.831155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum99
Range98
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.54851487
Coefficient of variation (CV)0.6525719272
Kurtosis17.03198999
Mean16.16452444
Median Absolute Deviation (MAD)8
Skewness2.359838556
Sum12929502
Variance111.2711659
MonotonicityNot monotonic
2025-08-22T17:40:27.880657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
27 26976
 
3.4%
21 26840
 
3.4%
13 26781
 
3.3%
3 26728
 
3.3%
19 26710
 
3.3%
18 26636
 
3.3%
20 26621
 
3.3%
24 26507
 
3.3%
17 26494
 
3.3%
4 26436
 
3.3%
Other values (22) 533140
66.7%
ValueCountFrequency (%)
1 24858
3.1%
2 26093
3.3%
3 26728
3.3%
4 26436
3.3%
5 25631
3.2%
ValueCountFrequency (%)
99 4031
 
0.5%
31 15356
1.9%
30 23711
3.0%
29 23315
2.9%
28 25824
3.2%

MES_CERT
Real number (ℝ)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.532448438
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:27.922793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q310
95-th percentile12
Maximum99
Range98
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.934988719
Coefficient of variation (CV)0.6023757793
Kurtosis107.5442435
Mean6.532448438
Median Absolute Deviation (MAD)3
Skewness4.635100924
Sum5225103
Variance15.48413622
MonotonicityNot monotonic
2025-08-22T17:40:27.962138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 77312
9.7%
12 75377
9.4%
6 69468
8.7%
11 66393
8.3%
3 65362
8.2%
5 65221
8.2%
8 64814
8.1%
10 64483
8.1%
7 64204
8.0%
2 63010
7.9%
Other values (3) 124225
15.5%
ValueCountFrequency (%)
1 77312
9.7%
2 63010
7.9%
3 65362
8.2%
4 61740
7.7%
5 65221
8.2%
ValueCountFrequency (%)
99 287
 
< 0.1%
12 75377
9.4%
11 66393
8.3%
10 64483
8.1%
9 62198
7.8%

ANIO_CERT
Real number (ℝ)

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2022.984918
Minimum2022
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:27.997346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2022
5-th percentile2023
Q12023
median2023
Q32023
95-th percentile2023
Maximum2023
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1218811201
Coefficient of variation (CV)6.024816054 × 10-5
Kurtosis61.31784283
Mean2022.984918
Median Absolute Deviation (MAD)0
Skewness-7.957241325
Sum1618122923
Variance0.01485500743
MonotonicityNot monotonic
2025-08-22T17:40:28.032287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
2023 787805
98.5%
2022 12064
 
1.5%
ValueCountFrequency (%)
2022 12064
 
1.5%
2023 787805
98.5%
ValueCountFrequency (%)
2023 787805
98.5%
2022 12064
 
1.5%
Distinct94
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:28.136367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length0
Mean length0.003870633816
Min length0

Characters and Unicode

Total characters3096
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
o961 69
 
8.9%
o960 48
 
6.2%
o721 47
 
6.1%
o998 47
 
6.1%
o970 43
 
5.6%
o150 40
 
5.2%
o994 28
 
3.6%
o995 25
 
3.2%
o882 25
 
3.2%
o142 24
 
3.1%
Other values (83) 378
48.8%
2025-08-22T17:40:28.296003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 760
24.5%
9 521
16.8%
1 367
11.9%
0 283
 
9.1%
2 210
 
6.8%
6 206
 
6.7%
8 186
 
6.0%
7 160
 
5.2%
5 155
 
5.0%
4 152
 
4.9%
Other values (4) 96
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3096
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 760
24.5%
9 521
16.8%
1 367
11.9%
0 283
 
9.1%
2 210
 
6.8%
6 206
 
6.7%
8 186
 
6.0%
7 160
 
5.2%
5 155
 
5.0%
4 152
 
4.9%
Other values (4) 96
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3096
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 760
24.5%
9 521
16.8%
1 367
11.9%
0 283
 
9.1%
2 210
 
6.8%
6 206
 
6.7%
8 186
 
6.0%
7 160
 
5.2%
5 155
 
5.0%
4 152
 
4.9%
Other values (4) 96
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3096
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 760
24.5%
9 521
16.8%
1 367
11.9%
0 283
 
9.1%
2 210
 
6.8%
6 206
 
6.7%
8 186
 
6.0%
7 160
 
5.2%
5 155
 
5.0%
4 152
 
4.9%
Other values (4) 96
 
3.1%
Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:28.357380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1599738
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row88
2nd row88
3rd row88
4th row88
5th row88
ValueCountFrequency (%)
88 715751
89.5%
99 35189
 
4.4%
15 5357
 
0.7%
08 4405
 
0.6%
11 4329
 
0.5%
14 3550
 
0.4%
19 2615
 
0.3%
16 2542
 
0.3%
26 2132
 
0.3%
30 2062
 
0.3%
Other values (24) 21937
 
2.7%
2025-08-22T17:40:28.448254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 1437674
89.9%
9 73554
 
4.6%
1 30343
 
1.9%
2 15653
 
1.0%
0 14251
 
0.9%
5 6854
 
0.4%
3 6792
 
0.4%
6 5488
 
0.3%
4 5261
 
0.3%
7 3868
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1599738
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 1437674
89.9%
9 73554
 
4.6%
1 30343
 
1.9%
2 15653
 
1.0%
0 14251
 
0.9%
5 6854
 
0.4%
3 6792
 
0.4%
6 5488
 
0.3%
4 5261
 
0.3%
7 3868
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1599738
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 1437674
89.9%
9 73554
 
4.6%
1 30343
 
1.9%
2 15653
 
1.0%
0 14251
 
0.9%
5 6854
 
0.4%
3 6792
 
0.4%
6 5488
 
0.3%
4 5261
 
0.3%
7 3868
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1599738
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 1437674
89.9%
9 73554
 
4.6%
1 30343
 
1.9%
2 15653
 
1.0%
0 14251
 
0.9%
5 6854
 
0.4%
3 6792
 
0.4%
6 5488
 
0.3%
4 5261
 
0.3%
7 3868
 
0.2%
Distinct404
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:28.603901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2399607
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)< 0.1%

Sample

1st row888
2nd row888
3rd row888
4th row888
5th row888
ValueCountFrequency (%)
888 715751
89.5%
999 35487
 
4.4%
037 2201
 
0.3%
017 1551
 
0.2%
007 1476
 
0.2%
020 1472
 
0.2%
005 1456
 
0.2%
039 1268
 
0.2%
018 1235
 
0.2%
001 1206
 
0.2%
Other values (394) 36766
 
4.6%
2025-08-22T17:40:28.811924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 2153916
89.8%
9 113667
 
4.7%
0 59042
 
2.5%
1 19792
 
0.8%
3 12458
 
0.5%
2 11130
 
0.5%
7 9146
 
0.4%
5 7941
 
0.3%
4 7366
 
0.3%
6 5149
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2399607
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 2153916
89.8%
9 113667
 
4.7%
0 59042
 
2.5%
1 19792
 
0.8%
3 12458
 
0.5%
2 11130
 
0.5%
7 9146
 
0.4%
5 7941
 
0.3%
4 7366
 
0.3%
6 5149
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2399607
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 2153916
89.8%
9 113667
 
4.7%
0 59042
 
2.5%
1 19792
 
0.8%
3 12458
 
0.5%
2 11130
 
0.5%
7 9146
 
0.4%
5 7941
 
0.3%
4 7366
 
0.3%
6 5149
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2399607
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 2153916
89.8%
9 113667
 
4.7%
0 59042
 
2.5%
1 19792
 
0.8%
3 12458
 
0.5%
2 11130
 
0.5%
7 9146
 
0.4%
5 7941
 
0.3%
4 7366
 
0.3%
6 5149
 
0.2%
Distinct234
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:28.994341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3199476
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8888
2nd row8888
3rd row8888
4th row8888
5th row8888
ValueCountFrequency (%)
8888 715751
89.5%
0001 35949
 
4.5%
9999 35700
 
4.5%
7777 8114
 
1.0%
0005 130
 
< 0.1%
0002 129
 
< 0.1%
0003 104
 
< 0.1%
0054 102
 
< 0.1%
0017 86
 
< 0.1%
0021 86
 
< 0.1%
Other values (224) 3718
 
0.5%
2025-08-22T17:40:29.217293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 2863773
89.5%
9 143308
 
4.5%
0 116547
 
3.6%
1 37824
 
1.2%
7 32968
 
1.0%
2 1354
 
< 0.1%
3 1134
 
< 0.1%
4 952
 
< 0.1%
5 926
 
< 0.1%
6 690
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3199476
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 2863773
89.5%
9 143308
 
4.5%
0 116547
 
3.6%
1 37824
 
1.2%
7 32968
 
1.0%
2 1354
 
< 0.1%
3 1134
 
< 0.1%
4 952
 
< 0.1%
5 926
 
< 0.1%
6 690
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3199476
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 2863773
89.5%
9 143308
 
4.5%
0 116547
 
3.6%
1 37824
 
1.2%
7 32968
 
1.0%
2 1354
 
< 0.1%
3 1134
 
< 0.1%
4 952
 
< 0.1%
5 926
 
< 0.1%
6 690
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3199476
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 2863773
89.5%
9 143308
 
4.5%
0 116547
 
3.6%
1 37824
 
1.2%
7 32968
 
1.0%
2 1354
 
< 0.1%
3 1134
 
< 0.1%
4 952
 
< 0.1%
5 926
 
< 0.1%
6 690
 
< 0.1%

RAZON_M
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)0.2%
Missing799285
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean1
Minimum1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:29.267792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum1
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean1
Median Absolute Deviation (MAD)0
Skewness0
Sum584
Variance0
MonotonicityIncreasing
2025-08-22T17:40:29.301947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
1 584
 
0.1%
(Missing) 799285
99.9%
ValueCountFrequency (%)
1 584
0.1%
ValueCountFrequency (%)
1 584
0.1%
Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-08-22T17:40:29.351559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2399607
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row999
2nd row999
3rd row999
4th row999
5th row999
ValueCountFrequency (%)
999 772391
96.6%
919 5632
 
0.7%
906 3059
 
0.4%
929 2168
 
0.3%
928 2148
 
0.3%
902 1292
 
0.2%
922 1276
 
0.2%
921 1222
 
0.2%
904 1068
 
0.1%
930 1010
 
0.1%
Other values (21) 8603
 
1.1%
2025-08-22T17:40:29.445849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 2352669
98.0%
2 12472
 
0.5%
1 11859
 
0.5%
0 9279
 
0.4%
6 4575
 
0.2%
8 2954
 
0.1%
4 2141
 
0.1%
3 1718
 
0.1%
5 1370
 
0.1%
7 570
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2399607
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 2352669
98.0%
2 12472
 
0.5%
1 11859
 
0.5%
0 9279
 
0.4%
6 4575
 
0.2%
8 2954
 
0.1%
4 2141
 
0.1%
3 1718
 
0.1%
5 1370
 
0.1%
7 570
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2399607
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 2352669
98.0%
2 12472
 
0.5%
1 11859
 
0.5%
0 9279
 
0.4%
6 4575
 
0.2%
8 2954
 
0.1%
4 2141
 
0.1%
3 1718
 
0.1%
5 1370
 
0.1%
7 570
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2399607
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 2352669
98.0%
2 12472
 
0.5%
1 11859
 
0.5%
0 9279
 
0.4%
6 4575
 
0.2%
8 2954
 
0.1%
4 2141
 
0.1%
3 1718
 
0.1%
5 1370
 
0.1%
7 570
 
< 0.1%